Machine Learning
Learn about the latest advances in machine learning that allow systems to learn and improve over time.
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Vanessa Parli, Stanford HAI Director of Research and AI Index Steering Committee member, notes that the 2025 AI Index reports flourishing and higher-quality academic research in AI.
Vanessa Parli, Stanford HAI Director of Research and AI Index Steering Committee member, notes that the 2025 AI Index reports flourishing and higher-quality academic research in AI.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
Whose Opinions Do Language Models Reflect?
Since the November 2022 debut of ChatGPT, language models have been all over the news. But as people use chatbots—to write stories and look up recipes, to make travel plans and even further their real estate business—journalists, policymakers, and members of the public are increasingly paying attention to the important question of whose opinions these language models reflect. In particular, one emerging concern is that AI-generated text may be able to influence our views, including political beliefs, without us realizing it. This brief introduces a quantitative framework that allows policymakers to evaluate the behavior of language models to assess what kinds of opinions they reflect.
Since the November 2022 debut of ChatGPT, language models have been all over the news. But as people use chatbots—to write stories and look up recipes, to make travel plans and even further their real estate business—journalists, policymakers, and members of the public are increasingly paying attention to the important question of whose opinions these language models reflect. In particular, one emerging concern is that AI-generated text may be able to influence our views, including political beliefs, without us realizing it. This brief introduces a quantitative framework that allows policymakers to evaluate the behavior of language models to assess what kinds of opinions they reflect.
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All Work Published on Machine Learning
Vanessa Parli, HAI Director of Research and AI Index Steering Committee member, speaks about the biggest takeaways from the 2025 AI Index Report.
Vanessa Parli, HAI Director of Research and AI Index Steering Committee member, speaks about the biggest takeaways from the 2025 AI Index Report.
Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.
Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.
In this brief, Stanford scholars introduce Holistic Evaluation of Language Models (HELM) as a framework to evaluate commercial application of AI use cases.
In this brief, Stanford scholars introduce Holistic Evaluation of Language Models (HELM) as a framework to evaluate commercial application of AI use cases.
Stanford HAI's 2025 AI Index Reveals Record Growth in AI Capabilities, Investment, and Regulation
"The AI Index equips policymakers, researchers, and the public with the data they need to make informed decisions — and to ensure AI is developed with human-centered values at its core," says Russell Wald, Executive Director of Stanford HAI and Steering Committee member of the AI Index.
"The AI Index equips policymakers, researchers, and the public with the data they need to make informed decisions — and to ensure AI is developed with human-centered values at its core," says Russell Wald, Executive Director of Stanford HAI and Steering Committee member of the AI Index.
Model-based reinforcement learning (MBRL) is a promising route to sampleefficient policy optimization. However, a known vulnerability of reconstructionbased MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods —including DreamerV3 and DreamerPro — with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning.
Model-based reinforcement learning (MBRL) is a promising route to sampleefficient policy optimization. However, a known vulnerability of reconstructionbased MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods —including DreamerV3 and DreamerPro — with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning.
